hrvatski

Extraction of nonlinear features from biomedical time-series using HRVFrame framework

Biomedical time-series (BTS) such as cardiac rhythm, electrocardiogram, electroencephalogram, etc., usually require in- depth analysis in order to determine the presence of disorder. Normal pattern of a particular BTS often possesses highly complex behavior and contains nonstationarities as a result of background biological processes. Modelling of normal patterns as well as disorders is troublesome because of the indefinite feature space – as any characteristic of the time-series might be considered a feature. A usual approach to determining which feature of the time-series should be analyzed is through an informed decision by a medical professional. This decision is somewhat arbitrary because in some cases there are no clear guidelines to which feature should be considered best for modelling of a particular BTS pattern. Nonlinear features of BTS have been recently developed such as: approximate entropy, sample entropy, spectral entropy, correlation dimension, spatial filling index, fractal dimension, and many others, which aim to better describe both the normal pattern as well as to distinguish normal patterns from disorders. To our knowledge, there is no freely available tool for extraction of many nonlinear features from BTS. This work aims to promote HRVFrame, a Java based open-source framework that allows users to extract a large number of nonlinear features (and a number of standard linear features) from BTS. Currently, HRVFrame is limited to feature extraction from cardiac rhthym, but upgrades for other BTS are planned. HRVFrame enables supervised learning and facilitates accurate model construction by extracting feature vectors to files that can be analyzed by standard data mining tools.

nonlinear dynamics ; cadiac arrhythmia ; feature extraction ; data mining

nije evidentirano

engleski

Extraction of nonlinear features from biomedical time-series using HRVFrame framework

Biomedical time-series (BTS) such as cardiac rhythm, electrocardiogram, electroencephalogram, etc., usually require in- depth analysis in order to determine the presence of disorder. Normal pattern of a particular BTS often possesses highly complex behavior and contains nonstationarities as a result of background biological processes. Modelling of normal patterns as well as disorders is troublesome because of the indefinite feature space – as any characteristic of the time-series might be considered a feature. A usual approach to determining which feature of the time-series should be analyzed is through an informed decision by a medical professional. This decision is somewhat arbitrary because in some cases there are no clear guidelines to which feature should be considered best for modelling of a particular BTS pattern. Nonlinear features of BTS have been recently developed such as: approximate entropy, sample entropy, spectral entropy, correlation dimension, spatial filling index, fractal dimension, and many others, which aim to better describe both the normal pattern as well as to distinguish normal patterns from disorders. To our knowledge, there is no freely available tool for extraction of many nonlinear features from BTS. This work aims to promote HRVFrame, a Java based open-source framework that allows users to extract a large number of nonlinear features (and a number of standard linear features) from BTS. Currently, HRVFrame is limited to feature extraction from cardiac rhthym, but upgrades for other BTS are planned. HRVFrame enables supervised learning and facilitates accurate model construction by extracting feature vectors to files that can be analyzed by standard data mining tools.

nonlinear dynamics ; cadiac arrhythmia ; feature extraction ; data mining

nije evidentirano